#!/usr/bin/env python3
"""
{{.ProjectName}} Python Worker
"""

import sys
import os
import random

# Add pyproc_worker to path (adjust path as needed)
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../'))

from pyproc_worker import expose, run_worker

@expose
def predict(req):
    """
    Sample prediction function.
    
    Args:
        req: Dict with 'text' key
    
    Returns:
        Dict with prediction results
    """
    text = req.get('text', '')
    
    # Simulate some ML prediction logic
    score = random.random()
    confidence = random.uniform(0.7, 1.0)
    labels = ['positive', 'negative', 'neutral']
    label = random.choice(labels)
    
    return {
        'score': score,
        'confidence': confidence,
        'label': label,
        'text_length': len(text)
    }

@expose
def process_batch(req):
    """
    Process a batch of items.
    
    Args:
        req: Dict with 'items' list
    
    Returns:
        Dict with processed results
    """
    items = req.get('items', [])
    
    results = []
    for item in items:
        # Process each item
        result = {
            'id': item.get('id'),
            'processed': True,
            'score': random.random()
        }
        results.append(result)
    
    return {
        'results': results,
        'total': len(results),
        'success': True
    }

@expose
def analyze(req):
    """
    Analyze input data.
    
    Args:
        req: Dict with analysis parameters
    
    Returns:
        Dict with analysis results
    """
    data = req.get('data', {})
    options = req.get('options', {})
    
    # Perform analysis
    analysis = {
        'summary': f"Analyzed {len(data)} data points",
        'metrics': {
            'mean': random.uniform(0, 100),
            'std': random.uniform(0, 20),
            'min': random.uniform(0, 50),
            'max': random.uniform(50, 100)
        },
        'recommendations': [
            "Consider increasing sample size",
            "Review outlier detection parameters"
        ]
    }
    
    return analysis

if __name__ == '__main__':
    # Run the worker
    run_worker()